Over the past two decades, the importance of AUC has raised an increasing favor in the machine learning community to explore direct AUC optimization methods, e.g., [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. However, to our knowledge, the vast majority of related studies merely focus on the binary class scenario. Since real-world pattern recognition problems often involve more than two classes, it is natural to pursue its generalization in the world of multiclass problems. To this end, we present a very early study to the problem of AUC guided machine learning framework under the multiclass setting. Specifically,
we propose a universal empirical surrogate risk minimization framework with theoretical guarantees.
